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A Survey of Knowledge Representation Learning Based on Structure and Semantics

Published: 25 August 2022 Publication History

Abstract

Knowledge representation methods have played an important role in the field of artificial intelligence especially in machine learning and deep learning. It converts useful information such as images, texts, and languages into low-dimensional and dense entity vectors, and provides NLP with better updated ideas and improves computational efficiency. In order to understand the current knowledge representation learning methods and status, this paper analyzes and categorizes the knowledge representation model based on structure and semantics, and finds that the knowledge represented by graph is easy to understand, but there are high complexity and long-tailed distribution, and semantic information of the relationship is difficult to obtain. Therefore, the semantic composition method of relation is adopted to solve this problem.

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  1. A Survey of Knowledge Representation Learning Based on Structure and Semantics

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    cover image ACM Other conferences
    ICVARS '22: Proceedings of the 2022 6th International Conference on Virtual and Augmented Reality Simulations
    March 2022
    119 pages
    ISBN:9781450387330
    DOI:10.1145/3546607
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    Publication History

    Published: 25 August 2022

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    Author Tags

    1. attention knowledge graph
    2. entity alignment
    3. knowledge representation learning
    4. triplet classification

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    • Fundamental Research Funds for the Central Universities
    • Natural Science Fund of GanSu Province

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    ICVARS 2022

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